These over-sampling methods share a common limitation: since they interpolate between same-class instances, they do not expand the feature ranges of the training data. It is well-known, for example, that the SMOTE-based methods do not generate instances outside the convex-hull of the individual minority class [1]}. Hence, they can adjust class weight norms, but do not address the generalization gap as defined herein.
